function LoadParameters( tSVMTrainer )
	%
	tSVMTrainer.fTrainingVsTestSetDimensionsRatio	= 0.6;
	tSVMTrainer.iThresholdForDiscardingEventsClass	= 3; % to be kept, the class should be smaller than this
	%
	% 	-s svm_type : set type of SVM (default 0)
	% 		0 -- C-SVC
	% 		1 -- nu-SVC
	% 		2 -- one-class SVM
	% 		3 -- epsilon-SVR
	% 		4 -- nu-SVR
	% 	-t kernel_type : set type of kernel function (default 2)
	% 		0 -- linear: u'*v
	% 		1 -- polynomial: (gamma*u'*v + coef0)^degree
	% 		2 -- radial basis function: exp(-gamma*|u-v|^2)
	% 		3 -- sigmoid: tanh(gamma*u'*v + coef0)
	% 		4 -- precomputed kernel (kernel values in training_instance_matrix)
	% 	-d degree : set degree in kernel function (default 3)
	% 	-g gamma : set gamma in kernel function (default 1/num_features)
	% 	-r coef0 : set coef0 in kernel function (default 0)
	% 	-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
	% 	-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
	% 	-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
	% 	-m cachesize : set cache memory size in MB (default 100)
	% 	-e epsilon : set tolerance of termination criterion (default 0.001)
	% 	-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
	% 	-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
	% 	-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
	% 	-v n : n-fold cross validation mode (n must >= 2)
	% 	-q : quiet mode (no outputs)
	%
	% notice we do multi-class one-against-all classification 
	tSVMTrainer.strSvmlibOptions = '-q -c 1 -t 1 -d 2 -g 0.2 -b 1';
	%
end % function

